Data warehousing services are a form of data management, which is designed to enable and support Business Intelligence (BI) activities such as data engineering, analytics, and being a central repository for information to be analysed and actioned.

There are a number of services available, ranging from simple to use formats designed for beginners, to advanced and highly technical. Two popular data warehousing solutions are Databricks and Amazon Redshift.

Both Databricks and Amazon Redshift have developed substantially beyond their earlier positioning. Databricks now combines data engineering, SQL warehousing, streaming, governance and AI on a lakehouse platform, while Amazon Redshift offers both provisioned and serverless data warehousing within the AWS ecosystem. The choice is therefore less about headline market share and more about workload, architecture and operating model.

Why Databricks is popular

When it comes to handling data, whether it is a small amount or an increasingly large load, users want a program that is capable of managing the operation quickly, efficiently, and in a way that can scale up and down as required.

Databricks is a broad data and AI platform used for data engineering, analytics, streaming, SQL warehousing and machine learning. Its lakehouse architecture is designed to let teams work across these workloads on a shared data foundation.

• A significant knowledge base

• Guides

• Tutorials

• Documentation

The platform can be integrated with other leading data engineering tools, and distributed on a cloud computing environment, with flexibility in processing or using Spark’s native R, an SQL interface, Python, or Scala.

Databricks key benefits

There are a number of benefits to using Databricks for handling data coding, analytics, and other data science tasks, such as:

Notebook format keeps the data organised – By working on pieces in the Spark Notebook format, data is kept organised, accessible, and editable, with clusters being able to be adjusted, deleted, or moved through the intuitive dashboard.

Spark allows for aggregating large datasets in the cloud – Because Databricks allows for different formats of data, users have the ability to drop visuals in-line into notebooks, and allow for in-line graphs and visualisations.

Different cells can be set in different coding languages – The ability to operate a notebook with more than one coding language allows for innovate functionality, and to generate solutions to challenging run processes without having to move between formats or programs.

Why Amazon Redshift is popular

Offering efficient storage, high-performance query processing, scalable data warehousing and functionality, and the resources to run at high speeds even when handling petabytes, Amazon Redshift has proven to be a popular data solution for thousands of users.

Supported by:

• Extensive knowledge base

• External cloud hosting

• A range of complementary services and functions

Redshift is used by small and large operations, and although it is sometimes considered to be more technical, there are a number of learning options and scalable features that integrate to make the platform suitable for most.

Redshift key benefits

When using the Redshift platform, some of the most commonly referenced benefits include:

High-performance query processing – The resources available to the platform and users, allow for datasets to be handled with efficient storage and fast querying.

Setup is relatively easy – There is a significant amount of automation and integration in the platform, which allows setup, deployment, and management of tasks to be handled with automated provisioning – making it easier to use than some other platforms.

Payment is on a pay-as-you-go basis – There are a number of different payment options for the service, and with no up-front costs, users are only being charged for what they are using.

Data can be structured and centralised for time-efficient data queries – By utilising the AWS platform and the variety of tools available, data can be structured and organised to provide better insights and more effective use of time and resources.

Limitations and challenges of Databricks and Redshift

As with any technology, there are limitations and challenges to both Databricks and Redshift, depending on what the service is needed for, and how the user intends to utilise the functions.

Cons of Databricks

• The breadth of the platform can create a steeper learning curve when teams move beyond a single workload into engineering, SQL analytics, streaming, governance and AI.

• Cost and performance depend heavily on compute choices, workload design and governance, so teams need clear usage and optimisation practices as adoption grows.

• Production environments still require deliberate resilience, recovery and data lifecycle design rather than assuming platform-managed services remove the need for operational planning.

• The strongest fit depends on the workload: organisations focused primarily on conventional SQL warehousing may prefer a more warehouse-centric operating model.

Cons of Redshift

• Redshift is a managed AWS service, but organisations still need to make architecture and workload-management choices around data layout, integrations, cost and performance.

• Cost can vary materially with workload pattern, data scanned, capacity and whether provisioned or serverless compute is used, so active cost management remains important.

• The platform is not a multi-cloud solution

• Redshift is available in both provisioned and serverless deployment models. The trade-off is therefore between the control of provisioned capacity and the operational simplicity and elastic scaling of Redshift Serverless.

Alternative technologies to Databricks and Redshift

There are other technology partners that provide similar services to Databricks and Redshift, which may be more appropriate for different tasks, or as a complement to the existing service.

Some of the most popular options include:

Google BigQuery – Part of the Google Cloud suite of services, the technology allows for the handling of large volumes of data, and processing for business analytics, as well as having machine learning capabilities. The platform has been used by world-renowned brands, including – Renault, Macy’s and TUI Travel.

Snowflake –Although the Snowflake platform was not created to serve the same functions as Databricks, over time, there has been significant development in the service and areas of overlap which make Snowflake a popular choice when handling data needs.

Vertica –The Massive Parallel Processing (MPP) data warehouse platform has been designed to work with big data and is a popular choice for clients who are looking for options involving increasingly large data sets.

Many of the existing platforms and programs are capable of integrating with one another, but it is important that when determining what platform you chose that you look at what your team are working with, and whether they are capable of changing to a different format (should the software require it), and that the needs of the platform are scalable and cost-effective for both current and future needs of your business.

Explore data warehousing technologies, making the right choice

Ardent data warehousing service

Ardent have leveraged both Databricks and Amazon Redshift for multiple client projects with the technology chosen based on its fitting to client requirements. If you are dealing with large volumes of complex data and want to store it in an organised and accessible, we can help. Our data warehousing solution ensures your data is secure, scalable and accessible. Explore the stories of our clients succeeding with Ardent data engineering services:

• Improving data turnaround by 80% with Databricks for a Fortune 500 company

• Ensuring timely data availability for real-time, mission-critical data

• Robust, scalable data pipelines with AWS infrastructure driving insights for global brands

Get in touch to find out more or to get started on unlocking the potential of your data.